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An investigation of small and medium-sized enterprises’ flood preparation and insurance coverage using agent-based modelling

Lookup NU author(s): Professor Graham Coates

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Abstract

© 2018 WIT Press. Flooding can result in significant financial losses to a nation’s economy in part due to the damage and disruption caused to businesses. In the United Kingdom (UK), Small and Medium-sized Enterprises (SMEs) are crucial to the economy as they account for 99.9% of all businesses. In recognizing the economic importance of SMEs, and their limited resources and tendency to lack disaster mitigation plans, there is a need to investigate ways in which these businesses can improve in terms of flood preparation. An agent-based modelling approach is being developed to facilitate investigations into the effect of a range of precautions that SMEs can take in preparation for flooding. With a focus on manufacturing SMEs, agents representing these businesses have been modelled as having behaviours that can be enacted pre-and post-flood. Agent-based modelling and simulation (ABMS) has been used, coupled with a case study of the 2007 flood of the Lower Don Valley in Sheffield, to investigate the influence of different types of insurance coverage and financial status on the response and recovery of manufacturing SMEs exposed to different levels of flooding. As expected, results indicate that SMEs with comprehensive insurance and funds available, along with allocating employees to clean-up its premises, leads to a reduced recovery stage. However, in addition, results provide an indication of the degree of influence of combinations of these attributes on SME recovery.

Publication metadata

Author(s): Alharbi M, Coates G

Publication type: Article

Publication status: Published

Journal: WIT Transactions on the Built Environment

Year: 2018

Volume: 184

Pages: 143-152

Acceptance date: 02/04/2018

ISSN (print): 1743-3509

ISSN (electronic): 1746-4498

Publisher: WITPress

URL: https://doi.org/10.2495/FRIAR180141

DOI: 10.2495/FRIAR180141

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